• Journal of the Korean Society of Food Science and Nutrition
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• v.45 no.12
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• pp.1816-1822
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• 2016

This study investigated the quality characteristics and antioxidant activities of madeleine added with lentil powder (LP). Madeleine was prepared with flour levels of lentil powder (0, 20, 40, and 60%). The pH, moisture, and specific gravity of madeleine decreased with increasing amounts of LP, whereas loss rate increased. Hunter L and b values of crust decreased with increasing amounts of LP, whereas a value of crust increased (P<0.05). Hunter L and b values of crumb increased with increasing amounts of LP, whereas a value of crumb decreased (P<0.05). For texture of madeleine with increasing amounts of LP, hardness and adhesiveness increased, whereas springiness, cohesiveness, and chewiness were reduced. DPPH radical scavenging activity of LP madeleine significantly increased with increasing amounts of LP (P<0.05). In the sensory evaluation of appearance, color, flavor, texture, taste, and overall preference, madeleine with LP 20% showed the highest value. It is suggested that LP 20% madeleine could be substituted for wheat flour to improve madeleine quality.

• The Journal of the Korea Contents Association
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• v.7 no.10
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• pp.19-26
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• 2007

Since collaborative filtering has used the nearest-neighborhood method based on item preference it cannot only reflect exact contents but also has the problem of sparsity and scalability. The item-based collaborative filtering has been practically used improve these problems. However it still does not reflect attributes of the item. In this paper, we propose the method of associative group using the FP-Tree to solve the problem of existing recommendation system. The proposed makes frequent item and creates association rule by using FP-Tree without occurrence of candidate set. We made the efficient item group using $\alpha-cut$ according to the confidence of the association rule. To estimate the performance, the suggested method is compared with Gibbs Sampling, Expectation Maximization, and K-means in the MovieLens dataset.

• The Journal of the Korea Contents Association
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• v.10 no.7
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• pp.23-30
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• 2010

It is important for the strategy of cosmetic sales to investigate the sensibility and the preference degree in the environment that the makeup style has been changed focusing on the consumer center. We proposed the human sensibility ergonomics makeup recommendation system (MakeupRS) using the context sensor information applying the collaborative filtering technique as one of methods in the makeup style development centered on the consumer's sensibility and the preference. In the collaborative filtering technique, the Pearson correlation coefficient applying to the case amplification is used to calculate similarity weights between the users. To investigate the sensibility according to the effect of makeup styles, the makeup styles were analyzed in terms of 6 style factors, such as, the foundation, the color lens, the eye shadow, the eye lash, the cheek brusher, and the lipstick. Ultimately, this paper suggests empirical application to verify the adequacy and the validity with the human sensibility ergonomics makeup recommendation system.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.4 s.310
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• pp.50-57
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• 2006

In Electronic Commerce, the latest most of the personalized recommender systems have applied to the collaborative filtering technique. This method calculates the weight of similarity among users who have a similar preference degree in order to predict and recommend the item which hits to propensity of users. In this case, we commonly use Pearson Correlation Coefficient. However, this method is feasible to calculate a correlation if only there are the items that two users evaluated a preference degree in common. Accordingly, the accuracy of prediction falls. The weight of similarity can affect not only the case which predicts the item which hits to propensity of users, but also the performance of the personalized recommender system. In this study, we verify the improvement of the prediction accuracy through an experiment after observing the rule of the weight of similarity applying Vector similarity, Entropy, Inverse user frequency, and Default voting of Information Retrieval field. The result shows that the method combining the weight of similarity using the Entropy with Default voting got the most efficient performance.

• Proceedings of the Korea Information Processing Society Conference
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• 2019.05a
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• pp.273-276
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• 2019

There is a lot of information in our world, quick access to the most accurate information or finding the information we need is more difficult and complicated. The recommendation system has become important for users to quickly find the product according to user's preference. A social recommendation system using community detection based on label propagation is proposed. In this paper, we applied community detection based on label propagation and collaborative filtering in the movie recommendation system. We implement with MovieLens dataset, the users will be clustering to the community by using label propagation algorithm, Our proposed algorithm will be recommended movie with finding the most similar community to the new user according to the personal propensity of users. Mean Absolute Error (MAE) is used to shown efficient of our proposed method.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.187-206
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• 2007

The development of computer and information technology has been combined with the information superhighway internet infrastructure, so information widely spreads not only in special fields but also in the daily lives of people. Information ubiquity influences the traditional way of transaction, and leads a new E-commerce which distinguishes from the existing E-commerce. Not only goods as physical but also service as non-physical come into E-commerce. As the scale of E-Commerce is being enlarged as well. It keeps people from finding information they want. Recommender systems are now becoming the main tools for E-Commerce to mitigate the information overload. Recommender systems can be defined as systems for suggesting some Items(goods or service) considering customers' interests or tastes. They are being used by E-commerce web sites to suggest products to their customers who want to find something for them and to provide them with information to help them decide which to purchase. There are several approaches of recommending goods to customer in recommender system but in this study, the main subject is focused on collaborative filtering technique. This study presents a possibility of pre-evaluation for the prediction performance of customer's preference in collaborative filtering before the process of customer's preference prediction. Pre-evaluation for the prediction performance of each customer having low performance is classified by using the statistical features of ratings rated by each customer is conducted before the prediction process. In this study, MovieLens 100K dataset is used to analyze the accuracy of classification. The classification criteria are set by using the training sets divided 80% from the 100K dataset. In the process of classification, the customers are divided into two groups, classified group and non classified group. To compare the prediction performance of classified group and non classified group, the prediction process runs the 20% test set through the Neighborhood Based Collaborative Filtering Algorithm and Correspondence Mean Algorithm. The prediction errors from those prediction algorithm are allocated to each customer and compared with each user's error. Research hypothesis : Two research hypotheses are formulated in this study to test the accuracy of the classification criterion as follows. Hypothesis 1: The estimation accuracy of groups classified according to the standard deviation of each user's ratings has significant difference. To test the Hypothesis 1, the standard deviation is calculated for each user in training set which is divided 80% from MovieLens 100K dataset. Four groups are classified according to the quartile of the each user's standard deviations. It is compared to test the estimation errors of each group which results from test set are significantly different. Hypothesis 2: The estimation accuracy of groups that are classified according to the distribution of each user's ratings have significant differences. To test the Hypothesis 2, the distributions of each user's ratings are compared with the distribution of ratings of all customers in training set which is divided 80% from MovieLens 100K dataset. It assumes that the customers whose ratings' distribution are different from that of all customers would have low performance, so six types of different distributions are set to be compared. The test groups are classified into fit group or non-fit group according to the each type of different distribution assumed. The degrees in accordance with each type of distribution and each customer's distributions are tested by the test of ${\chi}^2$ goodness-of-fit and classified two groups for testing the difference of the mean of errors. Also, the degree of goodness-of-fit with the distribution of each user's ratings and the average distribution of the ratings in the training set are closely related to the prediction errors from those prediction algorithms. Through this study, the customers who have lower performance of prediction than the rest in the system are classified by those two criteria, which are set by statistical features of customers ratings in the training set, before the prediction process.

• Journal of Korean Ophthalmic Optics Society
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• v.21 no.1
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• pp.1-10
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• 2016

Purpose: The study was aimed to suggest the most effective color of the tinted lenses by evaluating the effect of the prescription with tinted lenses on the visual quality of the elderly at the age of the sixty or more. Methods: The visual acuity of fifty subjects at the age of sixty or more (17 males, 33 females with the averaged age of $71.0{\pm}6.3$) were corrected to have the visual acuity at a far distance of 0.5 or more using a trial lens frame, and non-tinted, brown-tinted, and gray-tinted lenses were randomly applied on the trial frame. The minimum legibility and minimum separability were measured at a far distance in the aspect of the visual acuity and calculated as LogMAR and then, the visual acuity was compared. The stereopsis and contrast sensitivity were also estimated at a near distance in the aspect of the visual function. The participants' preference for tinted lenses and their subjective symptoms of the visual perception and the movement were further surveyed. Results: The best minimum legibility and minimum separability was shown when wearing non-tinted lenses, and brown-tinted and gray-tinted lenses were in the next. The stereopsis and the contrast sensitivity at a near distance and the visual perception was the best when wearing brown-tinted lenses. It was surveyed that the subjective discomfort was the biggest when wearing gray-tinted lenses, and brown-tinted lenses were the best in the aspect of the subjective preference. Conclusions: As the result of this study, it was revealed that the visual acuity and visual function could be improved by the use of tinted ophthalmic lenses however, its change of visual acuity and visual function was not completely correlated with the subjective satisfaction. Therefore, the appropriate color of ophthalmic lenses should be selected in accordance with the individual visual perception and the main vision lifestyle in the elderly generation. From the present study, the use of non- or brown-tinted lens and brown- or gray-tinted lens can be recommended for distance work and near work, respectively, in the elderly generation under the illumination of about 1,000 lux.

• Journal of Korean Ophthalmic Optics Society
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• v.19 no.2
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• pp.163-169
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• 2014

Purpose: This study is for compared the change of corneal refractive power before and after wearing of rigid gas permeable contact lense with diagnostic method which is 1 D flatter than alignment fitting on right eye and alignment fitting on left eye for 2 months and investigate the preference. Methods: Twenty middle school and high school students (40 eyes) who had never worn a contact lense before for no corneal topographical change, no ocular disease, no experience of ophthalmic surgery and have normal tear amount were selected for this study and corneal refractive power were examined before wearing rigid gas permeable contact lense and adaptation status and corneal examination were performed after 10 days of wearing and after cheking up the continuation of wearing, all candidate wear contact lens 8 hours per day for 2 month and corneal refractive power were compared. Results: After 2 months of wearing with 1 D flatter than the alignment fitting on right eyes, there was significant difference in the central corneal refractive power was $43.84{\pm}1.33D$, flat K power was $43.05{\pm}1.29D$, and steep K power was $44.61{\pm}1.42D$ decreased than before wearing (p<0.001, 0.001, 0.047). The e-value of the principal meridians also shows statistically significant difference (p=0.037, 0.015). After 2 months of wearing with alignment fitting on left eyes, the central corneal refractive power was $44.40{\pm}1.26D$, flat K power was $43.57{\pm}1.23D$. and flat K e-value was $0.58{\pm}0.05$ which showed no statistically significant difference (p = 0.769, 0.614, 0.181). But steep K power was $45.25{\pm}1.36$, and steep K e-value was $0.45{\pm}0.18$ which shows statistically significant difference (p=0.018, 0.027). Conclusions: Consider the comfort, clear vision, dryness for preference fitting investment, 6 students (30%) prefer right eye which is 1 D flatter fitting, 14 students (70%) prefer left eye which is alignment fitting. For rigid gas permeable fitting needed for accurate examination and should prescribe the alignment fitting which is suitable for each cornea.

• Journal of the Korean Society of Safety
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• v.33 no.2
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• pp.52-60
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• 2018

This study compares the differences of the fit factor by the order of wearing preference between Particulate filtering facepiece respirators(PFFR) and glasses when participants wore simultaneously and a survey of physical and visual complaint. Recognition level about fit of respirators was investigated and the educational (before- and after-) effect of the fit factor. When participants wore PFFR and glasses, physical complaints were nose pressure, slipping, nose and ear pressure, ear pressure and rim loosen, the most highly physical complaints were nose pressure. Visual complaints were demister, blurry vision, dizziness, visual field, and lens dirty, the most highly visual complaints were demister. But, there was significant difference in physical complaint such as nose pressure(10.3%), slipping (23.0%), nose and ear pressure(14.3%), and rim loosen(16.2%), visual complaint such as visual field(13.8%) and lens dirty(32.4%). For the recognition of fit of respirators, respirators fitness, leak site, an initial point and an object, faulty factor, recognition level was higher. Fit factor was increased after education of proper wearing of respirator. Change of the fit factor was smaller compared to the normal breathing and after 6 actions in case of after education. Questionnaire consisted of general characteristics and physical/visual complaint, recognition of fit. Complaints were measured after the QNFT with multiple choices. Quantitative fit factor was measured by device and compared the result of (before- and after-) educational effect. Also, we selected to 6 actions (Normal breathing, Deep breathing, Bending over, Turning head side to side, Moving head up and down, Normal breathing) among 8 actions OSHA QNFT (Quantitative Fit testing) protocol to measure the fit factors. The fit factor was higher after the training (p=0.000). Descriptive statistics, paired t-test, and Wilcoxon analysis were performed to describe the result of questionnaire and fit test. (P=0.05) Therefore, it is necessary to investigate the quantitative research such as training program and glasses fitting factor about the wearing of PFFR and glasses simultaneously.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used